Michael Akinola

Over the years, I have learned to Be PROACTIVE; Be RESPONSIBLE AND TAKE RESPONSIBILITY; NEVER ENTERTAIN IDLENESS; and LOVE

CRISP-DM with detailed sample projects in Python and R

In this blog, I discussed the most widely-used analytics model, CRISP-DM with sample projects in Python and R.

Working with Git and GitHub

This post describes how to use Git and GitHub to work and connect remotely to your projects. Feel free to use, like and leave comments.

Generative AI: A Simple Guide to the World of Creative Machines

Welcome to the fascinating world of Generative Artificial Intelligence (AI)! This post is designed to break down the basics of generative AI in a way that is easy to understand.

Meet Michael Akinola

I am a data scientist, currently deploying advanced data analytics techniques and machine learning algorithms to build predictive models for real world applications. My mantra is value creation, and I have consistently added value through problem solving, tutoring, coaching, guiding and empowering individuals to aspire to greatness. I am passionate about learning, research, innovation, personal development and leadership. I hold bachelor’s and master’s degrees in Physics and a master's degree in Computer Science & Quantitative Methods. I am committed to life-long learning and sharing knowledge with others. I enjoy leading diverse teams and drawing on the diverse strengths of everyone on the team. A proud husband and father, who enjoy playing scrabble, chess, piano, and soccer at my leisure. Various life experiences have taught me the importance of honesty, discipline, hard work, and perseverance. My goals are to contribute significantly to Artificial Intelligence development and to inspire the sleeping giant within every individual I meet.

Resume

Skills Summary

RPythonData ScienceData AnalysisMachine LearningTeam Leadership & Development

Projects

Exploratory Data Analysis of UCI Diabetes Dataset in R

Collaborative

Exploratory Data Analysis (EDA) is an approach for data analysis that often uses statistical graphics and other data visualization methods to maximize insight into a data set. In this work, we started by importing the data into R. Then probed and explored the data by inspecting the variable types, outlying and possibly wrong records, and other issues. We checked if there are any missing values which we applied list-wise deletion, we inspected the frequency distribution of the target variable class and see, e.g., whether we have an unbalanced classification problem. Learn, like and feel free to leave a comment.

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Integration of PCA and K-Means to Cluster Soccer Players into Similar Groups

Individual

This project uses European Soccer Database which has more than 25,000 matches and more than 10,000 players for European professional soccer seasons from 2008 to 2016. The exploratory data analysis includes some steps for exploring and cleaning the dataset, some steps for feature engineering using Pearson’s correlation coefficient with key attributes and domain knowledge, and some steps for grouping similar clusters using unsupervised machine learning algorithm, K-Means aided by Principal Components Analysis. Learn, like and feel free to leave a comment below.

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Customer Segmentation using Logistic Regression (R)

Collaborative

Machine learning has found application in various sectors of human endeavors of which marketing is not an exemption. Marketing is a key part of any business. It requires strategic steps to maximize its impact in today’s dynamic world using every available medium. In this work, we applied Logistic regression – a classification algorithm in machine learning to achieve this goal. Our goal is to achieve high true positive (TP) and reduce false negative or type 2 error. In other words, we want to have high tolerance on false positive or type 1 error. We used two methods in our choice of algorithm and evaluated them. Both methods gave accuracy of 0.70 but one method outshines the other in terms of error type reduction.

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Sentiment Analysis on Tweet Samples using Logistic Regression

Individual

From this notebook, you will learn about logistic regression. Concretely, you will learn how to implement logistic regression for sentiment analysis on tweets. Given a tweet, you will decide if it has a positive sentiment or a negative one.

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Let's have a chat

Meet Michael Akinola

Skills Summary

Projects

Exploratory Data Analysis of UCI Diabetes Dataset in R

Integration of PCA and K-Means to Cluster Soccer Players into Similar Groups

Customer Segmentation using Logistic Regression (R)

Sentiment Analysis on Tweet Samples using Logistic Regression

Let's have a chat

I will get back to you between 2 - 12 hours